Method for determining a touch event and touch sensitive device

ABSTRACT

The invention relates to a method for determining a touch event provided by a user on an interaction surface of a touch sensitive device comprising the steps of: sensing a raw signal by a transducer, in particular by a piezo-electric transducer and low-pass filtering the sensed signal, segmenting the filtered raw signal into a baseline signal and at least one useful signal, and analyzing the at least one useful signal to determine properties of the touch event.

The invention relates to a method for determining a touch event providedby a user on an interaction surface of a touch sensitive device. Theinvention furthermore relates to a touch sensitive device configured tocarry out this method.

A plurality of different technologies exists to provide touch sensitiveinterfaces. One can mention technologies such as capacitive, resistive,surface acoustic wave or acoustic technologies based on bending waves.These methods have in common that touch events, such as a dragginginteraction of a finger over the interaction surface and/or multi-touchtouch events such as a zoom-in or zoom-out touch gesture during whichtwo fingers move away or towards each other, are identified once the x,y coordinates of the touch points building up the touch event have beenidentified by the underlying analyzing method.

However, to determine the x, y coordinates of the various touch pointsof one touch event, the hardware and software necessary to identifythese complex gestures are rather demanding leading to a complex and,therefore, more expensive functionality. Thus touch sensitive deviceshaving this functionality are incorporated only in high end products.

Taking the acoustic technology based on bending waves created during thetouch event and travelling inside the touch sensitive interactionsurface towards transducers, it is for instance necessary to build updatabases with reference acoustic signals to be able to identify thelocations of each individual touch point building up the gesture.

To be able to introduce this kind of technology also in low costapplications or, in general, to be able to provide this functionality atlower cost, it is therefore the object of this invention to simplify themethod for determining a touch event.

This object is achieved with the method for determining a touch eventaccording to claim 1. It is the surprising finding of this inventionthat it is indeed possible to identify properties of a touch event basedon low pass filtered sensed signals. In the abovementioned acoustictechnologies, low frequencies have been rejected via high passfiltering. The reason for this rejection was the interference betweenthe low frequencies with noise. Now, by introducing an additional stepof segmenting the filtered raw signals into baseline signalsrepresenting the norm, a useful signal becomes possible to exploit lowfrequencies.

Here the term “touch event” relates to any interaction between a userand an interaction surface, which can be a single impact, provided by auser's finger or a stylus, a multi-touch event, e.g. by hitting theinteraction surface with more than one finger at the same time, but alsoto dragging events during which the user moves with his finger or thestylus over the interaction surface and which again can be a single or amulti-touch event. For instance, a single dragging touch event relatesto the swipe or flick gestures used in electronic devices and themulti-touch drag events relate to rotation, zoom-in and zoom-out touchevents.

According to a preferred embodiment, step b can comprise applying astatistical model to segment the filtered raw signal. Statistical modelshave the advantage that, without knowing the exact form of aninteraction, only knowing some characteristic differences between auseful signal and a baseline, it is possible to identify the one or theother class of event out of a signal that could be built up out of bothclasses. There is thus no need to establish a threshold value abovewhich the signal is considered to be a useful signal and below which thesignal is considered to be baseline/noise.

Preferably, the method can be carried out in the time domain. Thus notransformation into the frequency domain needs to be carried out whichsimplifies the hardware necessary to realize the method according to theinvention.

Preferably, the filter can have a maximum passing frequency of 100 Hz,in particular 50 Hz, more in particular 30 Hz. In this frequency range,a touch event occurring in the vicinity of a transducer leads to apattern that can be, on the one hand, exploited to identify the usefulsignal part of the raw signal data and, on the other hand, also todetermine certain properties of the touch event.

Further preferred, step b can comprise an expectation maximizationalgorithm, in particular a classification expectation maximizationalgorithm. These statistical methods are iterative techniques capable tomaximize the likelihood whenever data is incomplete and can be used incase of a mixture of data. This algorithm has the advantage that itconverges. In a variant, an additional classification is introducedforcing the probabilities of classes to occur at a given time to zero orone. The classification algorithm has the advantage that it convergesfaster and is therefore well suited to real time processing.

Preferably, it is assumed that both baseline and useful signals eachhave Gaussian distributions and that their corresponding signal energiesfollow Gamma distributions. Gaussian and Gamma distributions have knownproperties which simplify the programming of the algorithm so that thehardware necessary to realize the algorithm can be kept simple andprocessing speed kept high.

Preferably, during the segmentation step of the expectation maximizationalgorithm, the variances of the two distributions are based on signalenergy, wherein the useful signal class relates to maximal energy andthe baseline class relates to the median energy. It appears that theseinitial variances form a starting condition leading to a rapidconvergence of the expectation maximization algorithm.

Advantageously, during segmenting step b, a stream of a predeterminedduration of the sensed signal can be analysed. Typically, a gesture on amobile device such as zoom-in, zoom-out, rotate, swipe or flick gesturedoes not take more than 500 milliseconds. Therefore, it is sufficient toanalyse data streams of the sensed signal having a duration of, at most,two seconds. Indeed, by providing a data stream longer than a typicalgesture touch event, it is possible to always provide some baselineinformation, so that the CEM algorithm can converge and identify twodistributions.

Preferably, as soon as an useful signal has been identified during stepb, the useful signal can already be analyzed to be able to determineproperties of the corresponding touch event before the complete datastream has been analyzed. Thus, one does not have to await the end ofthe datastream before a command corresponding to a given gesture will beoutput. Therefore the method can be run under real time conditions.

Preferably the CEM algorithm can comprise a weighting step concerningthe initial condition at least of the baseline signal to improve theestimate of the input variance. In particular, in case the CEM algorithmhas not found a useful signal, the determined variance value can be usedas input variance for the baseline signal in the next run. Eventually amemory effect can be added.

Preferably, subsequent data streams of the sensed signal overlap. Thus,at least partially, already treated data is re-analysed again which canbe used to improve the reliability of the segmenting step of theinvention.

The method can advantageously be further configured such that a usefulsignal is transmitted to step c only if it has been identified at leasttwice. This will reduce the risk that a particular noise event willcause an inappropriate output.

Further preferred, if during step b the same useful signal has beenidentified in a predetermined amount of streams, in particular in two orthree streams, the following stream does not overlap with the part up tothe end of the useful signal of the preceding stream. Thus, once thepresence of a useful signal has been confirmed a predetermined amount oftimes, this part of the sensed signal does not need to be treated againso that calculation capacity can be saved which further helps to providereal time data analysis performance.

Preferably, an 80-95% signal overlap is performed in the methodaccording to the invention.

According to a preferred embodiment, the step c can comprise attributinga touch event as soon as a useful signal has been transmitted to step c.Thus, in case one is only looking for determining that a touch event,such as an impact, has occurred on the interaction surface, for instanceto switch on or off a certain functionality, the identification of theuseful signal is sufficient.

As for this kind of interaction, a very simple touch event sensingmethod can be provided. According to a preferred embodiment, step a cancomprise sensing raw signals by more than one, in particular four,transducers and carrying out step b for each one of the sensed signals.It was found out that, in the low frequency range analysed according tothe invention, the particular patterns observed in the sensed signalsupon reception of the touch event are observed in the vicinity of thetransducers. Thus, by providing a plurality of transducers, it willbecome possible to not only identify that an impact has occurred butclose to which transducer the touch event occurred and, of course, alsoat what time.

Further preferred, step c can comprise identifying various types ofgestures, in particular drag and its direction and/or rotate and senseof rotation and/or zoom-in and/or zoom-out, based on the time delaybetween one or more predetermined characteristics of the useful touchsignals identified during step b and/or their relative value for themore than one sensed raw signals. Preferably, the one or morepredetermined characteristics relate to the position of the maximal,minimal or signal average crossing of the useful signal. Thus, withoutcomplicated calculations or without having to establish a database and,more in particular, without having to identify the precise x and ycoordinates of the touch points building up a touch gesture touch event,it becomes possible by analyzing low frequency signals to identify thesekind of gestures by simply looking at the moment a useful signal occursat a given transducer and, eventually, the comparison of the absolutevalues of the special characteristics of the pattern.

According to a preferred embodiment, the type of gesture is onlydetermined based on the time delay and the positions of the transducerswith respect to the interaction surface. Thus, it becomes possible toprovide a cheap touch sensitive device capable of identifying evencomplex touch event gestures.

According to an advantageous embodiment of the invention, the method canfurther comprise a step of smoothing the useful signal prior to step c.By applying a smoothing step, e.g. use a further low pass filter,further noise reduction can be achieved to optimize the touch eventrecognition.

The object of the invention is also achieved with the computer programproduct comprising one or more computer readable media having computerexecutable instructions for performing steps b) and c) of the inventivemethod.

The object of the invention is also achieved with a touch sensitivedevice according to claim 19 and which is configured to identify thetouch event according to the method as described above. As aconsequence, a touch sensitive device can be provided that is capable ofidentifying even complex gestures such as multi-touch gestures while, atthe same time, the device can be kept cheap so that it can be used inlow end applications. This is particularly due to the fact that nopredetermined database is necessary and no complex data treatments. Theinteraction surface can be made out of any solid material, in particularout of glass, plastic or metal or a mixture thereof.

Furthermore, the object of the invention is achieved by a mobile devicecomprising a touch sensitive device as described above. In particular,for mobile devices which have a rather small form factor, all sorts oftouch events can be identified by the method as described above withouthaving to place a high number of transducers over the surface of thedevice. Typically, four to six transducers are sufficient to provide thedesired functionality.

According to an advantageous embodiment, the mobile device can have atouch sensitive device functionality at its front and/or back side,wherein a display device is provided on the front side. Thus, it becomespossible to provide input to the mobile device by carrying out a touchevent gesture either on the side of the display, but it is also possibleto provide the input via a touch gesture on the opposite side whichstill keeping the device simple.

In the following embodiments of the invention will be described indetail in conjunction with the accompanying figures:

FIG. 1 illustrates a touch sensitive device according to a firstembodiment of the invention,

FIG. 2 illustrates a typical signal sensed by a transducer when a touchevent occurs in the vicinity,

FIG. 3 illustrates a block diagram of a method according a secondembodiment of the invention,

FIG. 4 illustrates two useful signals identified on two differenttransducers and belonging to the same touch event,

FIG. 5 illustrates an example of an arrangement of four transducers onan interaction surface,

FIG. 6 illustrates a block diagram of a method according to a thirdembodiment of the_(e) invention.

FIG. 1 illustrates a touch sensitive device 1 according to a firstembodiment of the invention. The touch sensitive device 1 comprises aninteraction surface 3 and four transducers 5 a-5 d linked to a low passfilter unit 7 and a signal analyzing unit 9. The touch sensitive device1 can be part of an electronic device, like any type of mobile device,like a mobile phone, a MP3 player or any device needing a user interfacewith or without display. The touch sensitive device 1 is configured toidentify touch events provided by a user touching the interactionsurface, e.g. by touching the surface with one or more fingers, or bydragging one or more fingers over the surface of the interaction surfaceto identify their properties so that they can be classified as e.g. oneof a drag along a certain direction, e.g. a flick or swipe gesture or arotation together with the direction of rotation or a zoom-in orzoom-out gesture or a simple touch. The identified touch event can thenbe output via interface 11 to a controlling device of the electronicdevice.

The interaction surface 3 can be made of any solid material like glass,plastic or metal. The transducers 5 a to 5 d in this embodiment arepiezoelectric transducers but could also be a microphone or a MEMS. Theyserve to transform a signal travelling on or through the interactionsurface 3 into electric signals that can be analysed. The signal is theresult of a touch event provided by a user. Instead of four transducers,it is of course possible to use more or less transducers depending onthe applications of the touch sensing device 1 and the resolutionneeded. If one wants only to identify whether a touch event took placeor not, it is even possible to work with only one transducer 5 a.Furthermore, in this embodiment, the transducers 5 a to 5 d are arrangedon the side of the interaction surface 3 opposite to where a userprovides touch gestures.

The low pass filter unit 7 has a cut off frequency of maximal 100 Hz,but preferably a cut of frequency of 50 Hz or even better of 30 Hz andcan be an analogue or digital filter. The filtered raw signal sensed bythe transducers 5 a to 5 d will then be output to the signal analyzingunit 9.

In fact, a particular pattern, as illustrated in FIG. 2, can be observedin the very low frequency range at a transducer 5 a to 5 d, when touchevents occur near a transducer. The closer the touch event is to thetransducer the stronger the pattern. FIG. 2 actually illustrates theamplitude of the signal as a function of time with a cut-off frequencyof 30 Hz. In FIG. 2, five touch events can be identified.

The dependence of the signal on a transducers 5 a to 5 d proximity meansthat whenever a touch event, like a drag occurs near a given transducer,e.g. 5 a, it will induce a pattern similar to the one described above,and that when this drag moves to another transducer, e.g. 5 b, it willinduce a similar pattern near that transducer at a later time.

Without having to analyse the exact coordinates along which the touchevent occurs, it becomes thus possible to identify gestures likedescribed above. The problem of detecting a drag location is replaced bythe detection of the proximity between the dragging touch event or atouch and the transducers attached to the interaction surface 3. Henceit is possible to estimate an approximate drag location and itsdirection, which is sufficient for the typical applications of touchgestures used in electronic devices (switch on/off, flick, swipe,rotate, zoom), which do not need more exact location information. Thisis particularly true for applications with small surfaces as the numberof transducers necessary to identify these types of gestures can be keptlow, e.g. four like in this embodiment.

The corresponding analysis is carried out in the signal analyzing unit9. A second embodiment of the inventive method realized by the touchsensitive device 1 to identify properties of touch events, such thattheir classification can be carried out, will now be described in detailusing the block diagram of FIG. 3.

First of all the raw signals sensed by the transducers 5 a to 5 d arestored in an input buffer. The raw signals typically are sampled at asampling rate of 1000 Hz (step S1). In each analyzing run a data streamof a predetermined amount of sensed data points will be analysed. Inthis embodiment a datastream of 2048 sensed signal points correspondingto about 2 seconds is analysed.

Then a low pass filter (filtering unit 5) is applied to the raw sensedsignals of the data stream with a cutting frequency of 100 Hz,preferably 50 Hz, more preferably at 30 Hz (step S2). To reduce the datathat has to be analysed an optional step S3 can be introduced whichconsists in downsampling the sensed raw signals, e.g. by a downsamplingby a factor of 32. By doing so a data stream of about 2 seconds,representing about 2048 measurement points will be reduced to 64measurement points. This has the advantage that the amount of data to betreated can be reduced but still the touch events provided by a user canbe identified.

The next step S4 of the inventive method is the so called triggeringstep or segmenting step used to segment the filtered and downsampled rawsignal in periods of a baseline signal corresponding to noise andperiods of a useful signal corresponding to a touch event. The parts ofthe data stream corresponding to baseline will be rejected whereas theremaining segments carrying useful signals will be further processed bydata analyzing unit 9 during step S5.

In the following the triggering step S4 will be described in moredetail.

The main objective of the triggering step S4 is to segment the filteredraw signal data streams into baseline signals and useful signals. Thebasic hypothesis applied in this second embodiment is that the sensedsignal data follows a Gaussian distribution, whatever it is a baselineor a useful signal. Note that a kurtosis-based trigger assumes that onlybaseline data are Gaussian. The segmentation is carried out for each oneof the signals provided by each one of the transducers 5 a to 5 d. Thusin cases of four transducers, four data streams are analysed.

As a consequence the filtered and downsampled sensed signal output afterstep S3 is assumed to represent a mixture of two classes. The firstclass being the baseline and the second class being the useful signal,supposed to be induced by a touch event provided by a user and sensed byone or more transducers 5 a to 5 d. Triggering is thus equivalent tosegmentation, where it is desired to detect useful signals from amixture of a maximum of two classes of signals.

According to the invention an algorithm based on likelihoodmaximization, called Expectation Maximization (EM), is applied to carryout the segmentation. This is an iterative process, which consists inassigning automatically a class out of the two classes to everymeasurement point of the data stream currently under analysis.

The EM algorithm is usually used on mixture data. In other words, it issupposed to have a set of m classes {C_(k), k=1 . . . m}, and samplesx={x₁, x₂, . . . , x_(n)}, with x_(i) belonging to a class C_(k). Eachclass C_(k) is defined via its proper parameter vector Θ_(k) and its apriori probability π_(k). The problem consists in estimating theparameter vector Θ=[π₁, π₂, . . . π_(m), Θ₁, Θ₂, . . . Θ_(m)] using thelikelihood maximization. The general EM algorithm can be summarized asfollows:

-   -   1—Provide initial parameters Θ,    -   2—Estimate a posteriori probabilities,    -   3—Maximisation maximize the likelihood by assessing new        parameters,    -   4—Go to 2 until convergence

In fact, the log-likelihood that a sample x_(i) belongs to a class C_(k)is denoted by log(L(Θ_(k)/x_(i)))=log f(x_(i)/Θ_(k)), withf(x_(i)/Θ_(k)) being the probability that the sample x_(i) belongs tothe class C_(k). This implies that the log-likelihood of the vectorx={x₁, x₂, . . . , x_(n)} is given by

${\log \left( {L\left( {\Theta/x} \right)} \right)} = {\sum\limits_{i = 1}^{n}\; {\log \; {{f\left( {x/\Theta} \right)}.}}}$

When dealing with a mixture of m classes, the log-likelihood may bewritten as

${\log \left( {L\left( {\Theta/x} \right)} \right)} = {\sum\limits_{i = 1}^{n}\; {{\log \left( {\sum\limits_{k = 1}^{m}\; {\pi_{k}\; {f\left( {x_{i}/\Theta_{k}} \right)}}} \right)}.}}$

This form does not have any analytical solutions.

One way to solve this problem according to the invention is to introducen missing data z_(tk)=1 if the sample x_(i) is generated by the classC_(k), and z_(tk)=0 else. The introduction of the vector z simplifiesthe expression of the log-likelihood

${\log \left( {L\left( {{\Theta/x},z} \right)} \right)} = {\sum\limits_{i = 1}^{n}\; {\sum\limits_{k = 1}^{m}\; {z_{ik}\; {\log\left( {\pi_{k}\; {{f\left( {x_{i}/\Theta_{k}} \right)}.}} \right.}}}}$

Instead of an EM algorithm, it is also possible to apply its variation,the so called Classification-EM algorithm to the datastream. Thisalgorithm comprises in addition a classification step. The a posterioriprobabilities are equal to 0 and 1, using the maximum a posterioridecision rule (MAP).

The Classification-EM has the advantage that it is faster than the EMalgorithm, and is thus better adapted to real-time processing ofmeasurement data.

The Classification-EM algorithm may be expressed in the case of amixture as the following:

-   -   1—Initialization: Θ⁰=└π₁ ⁰, π₂ ⁰, . . . π_(m) ⁰, Θ₁ ⁰, Θ₂ ⁰, . .        . Θ_(m) ⁰┘    -   2—Estimation: Assessment of the a posteriori probabilities:

${\hat{z}}_{ik}^{(t)} = \frac{\pi_{k}^{(t)}{f\left( {x_{i};\Theta_{k}^{(t)}} \right)}}{\sum\limits_{l = 1}^{m}{\pi_{l}^{(t)}{f\left( {x_{i};\Theta_{l}^{(t)}} \right)}}}$

-   -   3—Classification: using the MAP decision rule

$u_{ik}^{(t)} = \left\{ \begin{matrix}1 & {{{si}\mspace{14mu} {\hat{z}}_{ik}^{(t)}} = {\max_{l}\left( {\hat{z}}_{il}^{(t)} \right)}} \\0 & {sinon}\end{matrix} \right.$

-   -   4—Maximization:

$\Theta^{({t + 1})} = {\arg \; {\max_{\Theta}{\sum\limits_{i = 1}^{n}\; {\sum\limits_{k = 1}^{m}\; {u_{ik}^{(t)}\; {\log \left( {\pi_{k}^{(t)}{f\left( {x_{i};\Theta_{k}^{(t)}} \right)}} \right)}}}}}}$

-   -   5—Go to 2 until convergence

According to the invention, one considers that the sensed, filtered anddownsampled raw data corresponds to a mixture of two Gaussian classes.The first class concerns the baseline (when no touch event is done) andthe second class denotes the useful signal (when a touch event occurs).One can furthermore make the assumption that these classes are zero-meanGaussian distributions, see e.g. FIG. 2. This means that every sample x,belongs to either N(0, σ₁ ²) or N(0, σ₂ ²), with a being the variance ofthe distribution. The invention is, however, not limited to identifyingtwo classes out of the gathered data. Eventually more than twodistributions with characteristic properties in the Gamma distributioncould of course also be identified. This could e.g. be of interest toidentify different types of impacts on the interaction surface.

To be able to separate the two distributions one looks at the signalenergy. Thus an energy transformation using n points of the datastreamsis used to enable the segmentation. In addition, the assumption is takenthat the samples are independent, which means that their energy followsa Gamma distribution. If independent x_(i)˜N(0, σ²), then

$y = {\sum\limits_{i = 1}^{n}x_{i}^{2}}$

follows a Gamma distribution

${f(y)} = {\frac{1}{\left( {2\sigma^{2}} \right)^{\frac{n}{2}}{\Gamma \left( \frac{n}{2} \right)}}y^{\frac{n}{2} - 1}{^{- \frac{y}{2\sigma^{2}}}.}}$

Initially, both classes are thus zero-mean Gaussian distributions withvariances σ₁ ² and σ₂ ². The complete Classification-EM algorithm canthen be written as follows:

-   -   1—Energy estimation on n points. The output is a mixture of N        points

$y = {\sum\limits_{i = 1}^{n}x_{i}^{2}}$

-   -   2—Initialization: The initial parameters vectors is Θ⁰=└π₁ ⁰, π₂        ⁰, σ₁ ⁰, σ₂ ⁰┘, with π_(i) denoting the a priori probabilities        for every distribution, and σ_(i) the corresponding standard        deviations at t=0. For {k=1, 2 and j=1 . . . N}    -   3—Estimate the a posteriori probabilities

${\overset{\Cap}{z}}_{jk}^{(t)} = \frac{\pi_{k}^{(t)}{f\left( {y_{j}/\sigma_{k}^{(t)}} \right)}}{\sum\limits_{l = 1}^{2}{\pi_{l}^{(t)}{f\left( {y_{j}/\sigma_{l}^{(t)}} \right)}}}$

-   -   4—Classification:

$u_{jk}^{(t)} = \left\{ \begin{matrix}1 & {{{if}\mspace{14mu} z_{jk}^{(t)}} = {\max_{l}\left( {\overset{\Cap}{z}}_{jl}^{(t)} \right)}} \\0 & {else}\end{matrix} \right.$

-   -   5—Update the parameters after the likelihood maximization:

$\sigma_{k}^{2} = {\frac{\sum\limits_{j}^{\;}{y_{j}u_{jk}^{(t)}}}{N{\sum\limits_{j}^{\;}u_{jk}^{(t)}}}\mspace{14mu} {and}}$$\pi_{k} = {\frac{1}{N}{\sum\limits_{j}^{\;}u_{jk}^{(t)}}}$

-   -   6—Go to 3 until convergence

The last equations in the method are obtained while searching for thelog-likelihood maximum, thus by calculating the derivative and settingit to zero. Maximize the log-likelihood consists on calculating thefollowing parameter vector

$\Theta^{({t + 1})} = {\arg \; {\max_{\Theta}{\sum\limits_{i = 1}^{n}\; {\sum\limits_{k = 1}^{m}\; {u_{ik}^{(t)}\; {\log \left( {\pi_{k}^{(t)}{f\left( {x_{i};\Theta_{k}^{(t)}} \right)}} \right)}\mspace{14mu} {with}}}}}}$${{f(x)} = {\frac{1}{\left( {2\sigma_{k}^{2}} \right)^{\frac{n}{2}}{\Gamma \left( \frac{n}{2} \right)}}x^{\frac{n}{2} - 1}^{- \frac{x}{2\sigma_{k}^{2}}}}},$

k=1, 2 refer to both Gamma distributions.

This consists in maximizing the following

$M = {\sum\limits_{i = 1}^{n}\; {\sum\limits_{k = 1}^{2}\; {u_{ik}\; {{\log\left( {\pi_{k}\; \frac{1}{\left( {2\sigma_{k}^{2}} \right)^{\frac{n}{2}}{\Gamma \left( \frac{n}{2} \right)}}{x_{i}}^{\frac{n}{2} - 1}^{- \frac{x_{i}}{2\sigma_{k}^{2}}}} \right)}.}}}}$

$\frac{\partial M}{\partial\sigma_{k}} = {{\frac{\partial\;}{\partial\sigma_{k}}\left( {\sum\limits_{i = 1}^{n}{u_{ik}\left\lbrack {{{- n}\; \log \; \sigma_{k}} - \frac{x_{i}}{2\sigma_{k}^{2}}} \right\rbrack}} \right)} = {{0\mspace{14mu} {gives}\mspace{14mu} \sigma_{k}^{2}} = {\frac{\sum\limits_{i = 1}^{n}{u_{ik}x_{i}}}{n{\sum\limits_{i = 1}^{n}u_{ik}}}.}}}$

The a priori probability π_(k) is updated as the ratio between thenumber of points belonging to the class C_(k) and the total amount ofmeasurement points.

According to the inventive embodiment, the signal energy in step 1— iscalculated using two consecutive points in the datastream.

Fixing initial conditions in step 2 permits guiding the algorithm todetect the desired classes. An assumption allowing a quick convergenceis to use the maximal energy and the median energy as initial variancesσ². The baseline class is assigned to the median energy and the usefulsignal class to the maximal energy. This choice has the advantage thatit is adaptive to the current situation. The assumption proved to workwell in identifying useful signals, when indeed the two classes werepresent in the analysed data stream. By taking a datastream that is longenough, as described above, this assumption can be satisfied in most ofthe cases and therefore prevent that a touch event and thus the usefulsignal that extents over a long duration will be classified as baseline.

As stated above, the next stage S5 of the inventive method determinesproperties of the touch event to be able to attribute a certain gestureto a given touch event. To do so one takes advantage of the time delaybetween corresponding useful signals for different transducers and/orthe relative amplitude of the maximum and/or the minimum of the usefulsignal.

In detail, first of all in S51 clusters of useful signals are detectedwithin the data stream window and among the four transducers 5 a to 5 c.To do so the procedure segments the data stream window into candidatetouch events by searching for common intervals among transducers. Inthis embodiment, two consecutive independent touch events are supposedto be spaced by at least 300 ms, a time duration which can of course beadapted to varying situations. This means that whenever two intervals,on two different transducers, are spaced by more than 300 ms, they areassigned to two different touch events.

Once the possible touch events have been identified in S51, the timedelay between transducers is analysed. This delay determines whether apattern precedes or succeeds another pattern. For this, a simpletechnique consists in comparing both the maximal and the minimal valuesfor both patterns. In fact, as the pattern may resemble to a sinusoidalperiod, there exists a maximum value, corresponding to its positivehalf-period and a minimal value corresponding to the negativehalf-period. The problem consists in determining whether the maximum ofpattern 1 precedes the maximum of pattern 2 and the minimum of pattern 1precedes the minimum of pattern 2. In this case, the transducer 1 isestimated to precede the transducer 2. When maximal and minimalpositions do not agree, the zero crossing position is used to estimatethe delay among patterns.

According to a variant, to be less perturbed by remaining noise, thedelay procedure is performed on a smoothed signal, which is a low passedfiltered version of the useful signal.

An example is illustrated in FIG. 4, showing the low frequency signal asa function of time observed on two transducers, e.g. 5 a with signal 41and 5 b with signal 43. It corresponds to a dragging touch event thatmoves thus from the transducer 5 a to the transducer 5 b. The dataanalyzing unit 9 will thus output a dragging touch event in thedirection transducer 5 a to transducer 5 b. Depending on the velocity,the data analyzing unit 9 will output a flick event or swipe event.

As already mentioned, in order to detect the type of the touch event, itis necessary to sort the transducers in terms of delay but also in termsof amplitude observed.

For a transducer configuration as illustrated in FIG. 5, showing theinteraction surface 3 and four transducers 5 a to 5 d, touch gestureswill be identified in the following way. For example, a specific dragmay give the following pattern sorting a) d) b) c), which means that apattern was first observed on transducer 5 a, then on transducer 5 d,then on transducer 5 b and finally on transducer 5 c. This findingimplies that a drag occurred from the right to the left and from theleft to the right. The drag passes from transducer 5 a to transducer 5 band from transducer 5 d to transducer 5 c. The analyzing unit 9 willidentify it as a dual drag touch event, which is typically classified asa zoom-out gesture. A zoom-out gesture consists in passing from right toleft, and from left to right, close to the center of the panel. Thismeans that a zoom-out gesture induces higher signal energies (+) fortransducers 5 a and 5 d than for transducers 5 b and 5 c (signal energyidentified as (−).

The fusion of amplitude information and transducer sorting is supposedto estimate the gesture type and its direction. More examples are givenin the following table:

sorting of signals according amplitude of Touch event to time delayuseful signal Zoom out a) then d) then b) then c) a) +; b) −; c) −; d) +Drag from left to right d) then c) then b) then a) a) +; b) −; c) −;d) + (in the middle) Drag from right to left a) then b) then c) then d)a) +; b) −; c) −; d) + (in the middle) Zoom in b) then c) then a) thend) or a) +; b) −; c) −; d) + c) then b) then a) then d) Drag from top toc) then a) then d) then b) or a) −; b) +; c) +; d) − bottom (in themiddle) c) then d) then a) then b) Impact close to 5d) d) Drag fromright to left a) then b) then c) then d) a) −; b) −; c) +; d) − (in theupper part of the interaction surface 3) Rotate right one finger a) thenb) then d) then c) a) +; b) +; c) +; d) + Rotate right two finger d)then a) then c) then b) or a) +; b) +; c) +; d) + a) then d) then b)then c)

This table is not exhaustive and other rules can be established toclassify the touch events. Whenever amplitude and transducer sorting arenot coherent, the analyzing unit 9 is configured to classify the touchevent as noise. The processing-decision stage may be improved dependingon each application of the inventive method. For example, whenever othergestures are desired or other transducer locations are tested, thecorresponding decision criteria will be adapted to build a table likeillustrated above.

It should be mentioned that the method according to this embodiment canbe configured such that as soon as an useful signal has been identified,the useful signal can already be analyzed to be able to determineproperties of the corresponding touch event before the complete datastream has been analyzed. Thus, one does not have to await the end ofthe datastream before a command corresponding to a given gesture will beoutput. Therefore the method can be run under real time conditions. Thesame is valid for a touch event that is rather long and extends beyondthe data stream window. For instance a slow scrolling down or up duringreading of a page shown on a screen, can also be identified by themethod.

According to a third embodiment of the invention, the method asillustrated in FIG. 3 is amended as illustrated in FIG. 6.

Steps S1 to S4 are carried out like in the first embodiment. Beforeproceeding to step S5, steps S2 to S4 are again carried out wherein anew data stream taken out of the input buffer (step S1) is analyzedwhich overlaps with the preceding one. For instance, the new data streamagain has 2048 points per transducer and has an overlap of about 94%thus of 1920 points. Of course the overlap can vary depending on theapplication.

To improve the classification of a touch event, step S5 is only carriedout if the same useful signal has been identified in two data streams(step S6). The predetermined amount of times can of course be adapted sothat the useful signal needs to present at least three or more times. Inthe example of 1920 point overlap, the same sequence can be analysed upto 16 times.

This is done in order to prevent remaining noise from causinginappropriate output by the signal analyzing unit 9.

On the other hand, this means that whenever a touch event is processedand detected up to 16 times, only the first two times are enough todecide that it is a gesture that will be classified in the next step S5.

Once the decision has been taken in step S6, it is then, according to afurther variant of the third embodiment, possible to take a data steamin the next analyzing run that does no longer overlap with the part ofthe preceding data stream that comprises the identified useful signal.

This variant has the advantage that the real time processing aspect canbe better realized, while at the same time reliability of the gesturedetection is kept.

Deciding with only 12.5% of the processing in case the useful signal hasbeen identified in the first two subsequent data streams, may indeed beconsidered as enough when external noise is acceptable. In case thenoise level is higher, the method can be adapted to proceed to step S5only in case the useful signal has been observed in more datastreams.The adaptation of the threshold can be automatic.

According to a fourth embodiment, the classification-EM algorithm theinitial conditions estimation in step 2— is adapted by taken intoaccount information from the previous processing steps of previousdatastream. Whenever the Classification-EM detects a baseline, it isobvious that its corresponding variance relates to the noise level, andtherefore, it can be used as estimate of the input variance for thebaseline class during the next step. Eventually a weighting step can beincluded such that the new baseline variance is a combination of initialbaseline variance and new variance. For instance the baseline variancecould be established by baseline variance=(n−1)/n preceding baselinevariance plus 1/n new variance. Thus, in particular, at the beginning ofthe process when the baseline is not yet known a rapid adaptation of thebaseline signal properties can be established.

It should be noted that the Classification-EM starts form the initialconditions and estimates during processing new variances that aresupposed to reflect the real variances and that all theseestimates—initial and final—are obtained automatically, and adapted toeach signal. Thus the process can be easily adapted to differentapplications, different form factors concerning the interaction surface,etc.

The touch sensitive device according to the invention, as well as theinventive methods have the advantage that without having to preciselyidentify the x, y coordinates of touch points building up a touch event,it is possibly to identify various types of touch gestures. This can bedone with a simple process based on statistical analysis of measured rawsignals in the low frequency domain. The identified useful signals arethen analysed according to rules concerning time delay between signalsand amplitudes. No comparison with predetermined databases are necessaryto identify acoustic signatures and no complicated data transformationsare necessary so that simple electronics can be used which make touchgestures available even for low end applications. The invention isparticularly advantageous on rather small interaction surfaces, likee.g. used in handheld devices, as there the number of transducersnecessary to identify the gestures can be kept small.

1. Method for determining a touch event provided by a user on aninteraction surface of a touch sensitive device comprising the steps of:a. sensing a raw signal by a transducer, in particular by apiezoelectric transducer and low-pass filtering the sensed signal, b.segmenting the filtered raw signal into a baseline signal and at leastone useful signal, and c. analyzing the at least one useful signal todetermine properties of the touch event, wherein step b) comprises anexpectation maximization algorithm (EM).
 2. Method according to claim 1,wherein step b) comprises applying a statistical model to segment thefiltered raw signal.
 3. Method according to claim 1, wherein step b) iscarried out in the time domain.
 4. Method according to claim 1, whereinthe filter has a maximal passing frequency of 100 Hz, in particular 50Hz, more in particular 30 Hz.
 5. Method according to claim 1, whereinstep b) further comprises a classification expectation maximizationalgorithm (CEM).
 6. Method according to claim 2, wherein to apply thestatistical model, it is assumed that both baseline and useful signalseach have a Gaussian distribution and their signal energies follow Gammadistributions.
 7. Method according to claim 6, wherein at theinitialisation step of the expectation maximization algorithm, thevariances of the two distributions are based on signal energy, whereinthe useful signal class relates to maximal energy and the baseline classrelates to the median energy.
 8. Method according to claim 1, whereinduring segmenting in step b) a stream of a predetermined duration of thesensed signal is analyzed.
 9. Method according to claim 8, whereinsubsequent streams overlap.
 10. Method according to claim 9, wherein ifduring step b) the same useful signal has been identified in apredetermined amount of streams, in particular in two or three streams,the following stream does not overlap with the part up to the end of theuseful signal of the precedent stream.
 11. Method according to claim 8,wherein the stream has a length of at most two seconds.
 12. Methodaccording to claim 1, wherein step c) comprises attributing a touchevent as soon a useful signal has been transmitted to step c.
 13. Methodaccording to claim 1, wherein step a) comprises sensing raw signals bymore than one, in particular four, transducers and carrying out step b)for each one of the sensed signals.
 14. Method according to claim 13,wherein step c) comprises identifying various types of gestures, inparticular drag and direction of drag and/or rotate and sense ofrotation and/or zoom-in and or zoom-out, based on the time delay betweenone or more predetermined characteristics of the useful touch signalsidentified during step b) and/or their relative values for the more thanone sensed raw signals.
 15. Method according to claim 14, wherein theone or more predetermined characteristics relate to the position of themaximal, minimal or signal average crossing of the useful signal. 16.Method according to claim 14, wherein the type of gesture is onlydetermined based on the time delay and the positions of the transducerwith respect to the interaction surface.
 17. Method according to claim1, further comprising a step of smoothing the useful signal prior tostep c).
 18. Computer program product comprising one or more computerreadable media having computer executable instructions for performing atleast steps b) and c) of claim
 1. 19. Touch sensitive device comprisingan interaction surface for receiving touch events provided by a user andat least one transducer, in particular a piezoelectric transducer, forsensing a signal, a low-pass filter unit and a signal analyzing unitconfigured to identify the touch event according to the method accordingto claim
 1. 20. Mobile device comprising a touch sensitive deviceaccording to claim
 19. 21. Mobile device according to claim 20, whereinthe front and/or backside of the mobile device comprises a touchsensitive device comprising an interaction surface for receiving touchevents provided by a user and at least one transducer, in particular apiezoelectric transducer, for sensing a signal, a low-pass filter unitand a signal analyzing unit configured to identify the touch event, andwherein a display device is provided on the front side.